CN109668588A - Air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor - Google Patents
Air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor Download PDFInfo
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- CN109668588A CN109668588A CN201910144270.2A CN201910144270A CN109668588A CN 109668588 A CN109668588 A CN 109668588A CN 201910144270 A CN201910144270 A CN 201910144270A CN 109668588 A CN109668588 A CN 109668588A
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Abstract
The present invention relates to a kind of air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor, comprising the following steps: correlation analysis is carried out to any two sensor in air cooling refrigeration unit；According to correlation analysis as a result, being grouped to refrigeration unit sensor and obtaining different groups；Virtual-sensor is constructed in each group；By virtual-sensor reading compared with respective sensor actual read number, if virtual-sensor reading mutates, there are failures for respective sensor.The present invention has rational design, it uses correlation analysis to analyze existing correlation between air cooling refrigeration unit sensor, the strong sensor of correlation is divided into one group, and virtual-sensor is constructed using long short-term memory Recognition with Recurrent Neural Network in group, by judging whether virtual-sensor reading mutates, to accurately and rapidly be positioned to refrigeration unit multisensor failure, energy waste is avoided, the service life of equipment is improved.
Description
Technical field
The invention belongs to air cooling refrigeration unit multi-sensor technology field, especially a kind of wind based on virtual-sensor
Cold type refrigeration unit sensor fault diagnosis method.
Background technique
With increasingly sharpening for world energy sources shortage problem, energy conservation and environmental protection has become current mostly important project, and builds
Industry is built as high-energy source and consumes industry, the influence to ecological environment can not be ignored.Energy consumption in construction industry is very big by one
It is derived partly from the energy consumption of building air conditioning after coming into operation, when air conditioning sensor breaks down, can lead to the energy of air-conditioning system
Loss-rate increases by 50% under normal circumstances.Therefore, energy saving reliability service of the refrigeration unit multiple sensor faults diagnosis to air-conditioning system
It plays a crucial role, it is in the urgent need to address at present for how detecting refrigeration unit multisensor failure accurately and timely
Problem.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, propose that a kind of design is reasonable and can sufficiently excavate refrigeration machine
The air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor of group multisensor behavioral characteristics.
The present invention solves its technical problem and adopts the following technical solutions to achieve:
A kind of air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor, comprising the following steps:
Step 1 carries out correlation analysis to any two sensor in air cooling refrigeration unit；
Step 2, according to correlation analysis as a result, being grouped to refrigeration unit sensor and obtaining different groups；
Step 3 constructs virtual-sensor in each group；
Step 4 reads virtual-sensor compared with respective sensor actual read number, if virtual-sensor reading occurs to dash forward
Become, then there are failures for respective sensor.
The sensor includes temperature sensor, flow sensor and the pressure sensor of air cooling refrigeration unit.
The step 1 carries out correlation analysis using pearson related coefficient method.
When the step 2 is grouped refrigeration unit sensor, the sensor by correlation greater than 0.5 is grouped into the same group
Not；If a sensor is both less than 0.5 with other all the sensors correlations, the maximum sensor of property associated therewith is chosen
Place group is group where the sensor.
The concrete methods of realizing of the step 3 are as follows: in different groups, recycle nerve net using long short-term memory respectively
Network constructs virtual-sensor.
The method of the long short-term memory Recognition with Recurrent Neural Network construction virtual-sensor are as follows: if containing n sensing in group
Device then constructs n long short-term memory Recognition with Recurrent Neural Network respectively, wherein neural network input is all sensings in same group
The measured value at T moment before device, neural network output are the corresponding virtual-sensor of different sensors, and T is any greater than 0
Number.
The judgment method that the step 4 virtual-sensor reading mutates are as follows: if current time virtual-sensor and reality
The difference of last moment virtual-sensor of the difference of border sensor reading greater than twice and real sensor reading, then it is assumed that virtual to pass
Sensor reading mutates.
The advantages and positive effects of the present invention are:
The present invention has rational design, uses correlation analysis to existing phase between air cooling refrigeration unit sensor
Closing property is analyzed, and the strong sensor of correlation is divided into one group, and long short-term memory Recognition with Recurrent Neural Network structure is utilized in group
Virtual-sensor is built out, by judging whether virtual-sensor reading mutates, thus accurately and rapidly more to refrigeration unit
Sensor fault is positioned, and energy waste is avoided, and improves the service life of equipment.
Detailed description of the invention
Fig. 1 is process flow diagram of the invention.
Specific embodiment
The embodiment of the present invention is further described below in conjunction with attached drawing.
A kind of air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor, as shown in Figure 1, include with
Lower step:
Step 1 carries out correlation point to any two sensor in air cooling refrigeration unit by pearson related coefficient
Analysis.
Sensor of the invention includes all the sensors such as temperature, flow, the pressure of air cooling refrigeration unit.For difference
The fault diagnosis of seed type sensor must train different long short-term memory Recognition with Recurrent Neural Network models, only deposit for synchronization
The sensor failure the case where.
Step 2, according to correlation analysis as a result, be grouped to refrigeration unit sensor, wherein correlation is greater than 0.5
Sensor is same group.
In this step, according to correlation analysis as a result, being grouped to refrigeration unit sensor, wherein correlation is greater than
0.5 sensor is same group, if a certain sensor C and other all the sensors correlations are both less than 0.5, selection and its
Group where the sensor of correlation maximum is group where C.
Step 3 constructs virtual-sensor in each group.
In this step, virtual-sensor is constructed using long short-term memory Recognition with Recurrent Neural Network in each group, in difference
Group in, if containing n sensor, be respectively necessary for a long short-term memory Recognition with Recurrent Neural Network of construction n, wherein neural network
Input is the measured value at T moment before all the sensors in same group, and T is the arbitrary number greater than 0, long short-term memory circulation mind
It is the corresponding virtual-sensor of different sensors through network output.
Step 4 reads virtual-sensor compared with respective sensor actual read number, if virtual-sensor reading occurs to dash forward
Become, then there are failures for respective sensor.
In this step, if the difference of current time virtual-sensor and real sensor reading is greater than twice of last moment
The difference of virtual-sensor and real sensor reading, then it is assumed that virtual-sensor reading mutates.
The present invention does not address place and is suitable for the prior art.
It is emphasized that embodiment of the present invention be it is illustrative, without being restrictive, therefore packet of the present invention
Include and be not limited to embodiment described in specific embodiment, it is all by those skilled in the art according to the technique and scheme of the present invention
The other embodiments obtained, also belong to the scope of protection of the invention.
Claims (7)
1. a kind of air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor, it is characterised in that including following
Step:
Step 1 carries out correlation analysis to any two sensor in air cooling refrigeration unit；
Step 2, according to correlation analysis as a result, being grouped to refrigeration unit sensor and obtaining different groups；
Step 3 constructs virtual-sensor in each group；
Step 4 reads virtual-sensor compared with respective sensor actual read number, if virtual-sensor reading mutates,
Then there are failures for respective sensor.
2. the air cooling refrigeration unit sensor fault diagnosis method according to claim 1 based on virtual-sensor,
Be characterized in that: the sensor includes temperature sensor, flow sensor and the pressure sensor of air cooling refrigeration unit.
3. the air cooling refrigeration unit sensor fault diagnosis method according to claim 1 based on virtual-sensor,
Be characterized in that: the step 1 carries out correlation analysis using pearson related coefficient method.
4. the air cooling refrigeration unit sensor fault diagnosis method according to claim 1 based on virtual-sensor,
It is characterized in that: when the step 2 is grouped refrigeration unit sensor, the sensor that correlation is greater than 0.5 being divided into same
Group；If a sensor is both less than 0.5 with other all the sensors correlations, the maximum sensing of property associated therewith is chosen
Group where device is group where the sensor.
5. the air cooling refrigeration unit sensor fault diagnosis method according to claim 1 based on virtual-sensor,
It is characterized in that: the concrete methods of realizing of the step 3 are as follows: in different groups, recycle nerve using long short-term memory respectively
Net structure virtual-sensor.
6. the air cooling refrigeration unit sensor fault diagnosis method according to claim 5 based on virtual-sensor,
It is characterized in that: the method for the long short-term memory Recognition with Recurrent Neural Network construction virtual-sensor are as follows: if containing n sensing in group
Device then constructs n long short-term memory Recognition with Recurrent Neural Network respectively, wherein neural network input is all sensings in same group
The measured value at T moment before device, neural network output are the corresponding virtual-sensor of different sensors, and T is any greater than 0
Number.
7. the air cooling refrigeration unit sensor fault diagnosis method according to claim 1 based on virtual-sensor,
It is characterized in that: the judgment method that the step 4 virtual-sensor reading mutates are as follows: if current time virtual-sensor and reality
The difference of last moment virtual-sensor of the difference of border sensor reading greater than twice and real sensor reading, then it is assumed that virtual to pass
Sensor reading mutates.
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| CN111652375A (en) * | 2020-06-02 | 2020-09-11 | 中南大学 | Intelligent detection and diagnosis method and device for cooling coil faults based on Bayesian inference and virtual sensing |
| WO2022079686A1 (en) * | 2020-10-15 | 2022-04-21 | Abb Schweiz Ag | Online frequently derived measurements for process monitoring, control and optimization |
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